Techno-economic optimization of grid-connected solar-wind-pumped storage hybrid energy system using improved search space reduction algorithm

• Published in: Journal of energy storage Vol. 52; p. 104778
• Main Authors: Bhimaraju, Ambati, Mahesh, Aeidapu, Joshi, Sukhdev Nirbheram
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2022
• Subjects: Hybrid renewable energy systems; Improved search space reduction algorithm; Optimal sizing; Pumped storage hydro-power station; Improved search space reduction algorithm; Optimal sizing; Hybrid renewable energy systems; Pumped storage hydro-power station
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2022.104778

With the fast paced development and integration of the renewable sources into the grid, it is essential to design and develop the storage system effectively to complement the renewable power generation so that the renewable energy system is not only reliable but also cost-effective. The pumped storage is an optimal, economically viable, and scalable solution for renewable energy integration with the grid. This paper proposes the optimal sizing of grid-connected solar–wind hybrid renewable energy systems (HRES) involving pumped storage hydro-power station (PSHS). The main goal is to determine the optimal combination of the PV-WT-PSHS hybrid system to minimize the levelized cost of energy (LCOE) subjected to the constraints which improve the system reliability and also promotes the use of maximum renewable fraction to meet the total load requirement. Further, the random inflow during the rainy season is modeled and its impact on the sizing of the HRES is also analyzed in this work. The effect of random inflow on sizing of the HRES is novel and has not been applied any optimal sizing studies in the literature. The optimal sizing is carried out by using a newly proposed algorithm called search space reduction (SSR) algorithm. Further, an improved search space reduction (ISSR) algorithm is also developed and applied to the same problem. The optimization is carried out in two scenarios; with and without including the random inflow, and the results indicate that when the random inflow is included, the cost per unit is significantly lesser. For validating the effectiveness of the ISSR algorithm, the results were compared to the other well-known algorithms such as grey wolf optimizer (GWO), teaching-learning based optimization (TLBO) the obtained also demonstrate the superiority of ISSR algorithm as it is able to achieve minimum value of objective function when compare to the other algorithms. •Sizing of PV-WT-PSHS hybrid system is carried out while including random inflow.•An improved search space reduction algorithm has been developed for optimization.•The inclusion of random inflow resulted in a cost reduction of 4.652%.•The designed system robustness is analyzed with uncertainty in weather conditions.